Some image editing applications allow designers to stylize the content of an image based on the style of another image. For instance, the style of a painting done by an artist may be transferred to a logo of a website so that the logo appears on the website as if it was painted by the artist. These image editing applications, however, are limited to images represented by raster graphics, and are not designed to directly process images or artwork represented by vector graphics (e.g., Bezier curves). Unlike vector graphics, raster images suffer from a degradation in quality when the image is scaled in size. Since it is difficult or impossible to transform most raster images to vector graphics, the output of these image editing applications is not easily converted from raster to vector graphics.
To transfer the style of one image to another image, most of these image editing applications rely on a deep learning model, such as a neural network that extracts feature maps from the raster images, combines the feature maps, and generates a raster image from the combined feature maps. Because the neural network is trained using raster images including real-world objects, these image editing applications usually fail when provided vector artworks, since the vector artworks generally include shapes defined by curves that are not recognizable by the trained neural network. These shortcomings are exacerbated for complex vector artworks represented by large numbers of curves (e.g., thousands of curves).
Furthermore, the deep learning models are computationally expensive, requiring significant amounts of training data and time to process the training data. When deployed in an image editing application, the trained model often requires so many calculations that it cannot be implemented without significant processing delay, preventing a real-time experience for the user and making the image editing application frustrating to operate.
Moreover, the deep learning models are generally not robust and produce unpredictable and unacceptable results. For instance, the deep learning models often fail for “unseen” classes of objects that are not included in the training images. These deep learning models may also introduce undesirable distortions in the stylized image, such as loss of information, structural distortion, blurring, direct copying of undesirable features, compression artifacts, and the like.
Because of the advantages of vector graphics over raster graphics (e.g., scalability without loss), some image editing applications directly operate on vector graphics artwork rather than images represented by raster graphics. However, due to the infeasibility of converting raster graphics to vector graphics and the shortcomings of deep learning models used for style transfer as discussed above, these image editing applications do not include functions to transfer the style of one vector artwork to another vector artwork. Accordingly, image editing applications, whether processing images represented by raster graphics or artwork represented by vectors, are not suitable to transfer the style of one vector artwork to the content of another artwork.